A multi-fidelity surrogate model based on moving least squares: fusing different fidelity data for engineering design

[1]  Andy J. Keane,et al.  Engineering Design via Surrogate Modelling - A Practical Guide , 2008 .

[2]  P. A. Newman,et al.  Approximation and Model Management in Aerodynamic Optimization with Variable-Fidelity Models , 2001 .

[3]  Wei Sun,et al.  A radial basis function-based multi-fidelity surrogate model: exploring correlation between high-fidelity and low-fidelity models , 2019, Structural and Multidisciplinary Optimization.

[4]  Nam H. Kim,et al.  Issues in Deciding Whether to Use Multifidelity Surrogates , 2019, AIAA Journal.

[5]  Alexander I. J. Forrester,et al.  Multi-fidelity optimization via surrogate modelling , 2007, Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences.

[6]  Yan Wang,et al.  A sequential multi-fidelity metamodeling approach for data regression , 2017, Knowl. Based Syst..

[7]  Stefan Görtz,et al.  Hierarchical Kriging Model for Variable-Fidelity Surrogate Modeling , 2012 .

[8]  Guangyao Li,et al.  Multi-fidelity optimization for sheet metal forming process , 2011 .

[9]  Shaojun Feng,et al.  Adaptive infill sampling criterion for multi-fidelity gradient-enhanced kriging model , 2020 .

[10]  Yan Liu,et al.  Improving surrogate-assisted variable fidelity multi-objective optimization using a clustering algorithm , 2014, Appl. Soft Comput..

[11]  Jian Li,et al.  Doubly weighted moving least squares and its application to structural reliability analysis , 2012 .

[12]  Bernhard Schölkopf,et al.  A tutorial on support vector regression , 2004, Stat. Comput..

[13]  Shaojun Feng,et al.  Adaptive gradient-enhanced kriging model for variable-stiffness composite panels using Isogeometric analysis , 2018 .

[14]  G. Gary Wang,et al.  Review of Metamodeling Techniques in Support of Engineering Design Optimization , 2007 .

[15]  P. Lancaster,et al.  Surfaces generated by moving least squares methods , 1981 .

[16]  Donald R. Jones,et al.  Efficient Global Optimization of Expensive Black-Box Functions , 1998, J. Glob. Optim..

[17]  Rampi Ramprasad,et al.  Multifidelity Information Fusion with Machine Learning: A Case Study of Dopant Formation Energies in Hafnia. , 2019, ACS applied materials & interfaces.

[18]  David J. J. Toal,et al.  Some considerations regarding the use of multi-fidelity Kriging in the construction of surrogate models , 2015 .

[19]  R. Haftka Combining global and local approximations , 1991 .

[20]  Chang Yong Song,et al.  Role of Conservative Moving Least Squares Methods in Reliability Based Design Optimization: A Mathematical Foundation , 2011 .

[21]  Andy J. Keane,et al.  Recent advances in surrogate-based optimization , 2009 .

[22]  P. Villon,et al.  Moving least squares response surface approximation: Formulation and metal forming applications , 2005 .

[23]  Piotr Breitkopf,et al.  On-the-fly model reduction for large-scale structural topology optimization using principal components analysis , 2020, Structural and Multidisciplinary Optimization.

[24]  Jack P. C. Kleijnen,et al.  Response surface methodology for constrained simulation optimization: An overview , 2008, Simul. Model. Pract. Theory.

[25]  Michael S. Eldred,et al.  Second-Order Corrections for Surrogate-Based Optimization with Model Hierarchies , 2004 .

[26]  Raphael T. Haftka,et al.  Low-fidelity scale factor improves Bayesian multi-fidelity prediction by reducing bumpiness of discrepancy function , 2018, Structural and Multidisciplinary Optimization.

[27]  R. Haftka,et al.  Ensemble of surrogates , 2007 .

[28]  Timothy W. Simpson,et al.  Metamodeling in Multidisciplinary Design Optimization: How Far Have We Really Come? , 2014 .

[29]  H. Fang,et al.  Global response approximation with radial basis functions , 2006 .

[30]  Stefan Görtz,et al.  Improving variable-fidelity surrogate modeling via gradient-enhanced kriging and a generalized hybrid bridge function , 2013 .

[31]  I A Basheer,et al.  Artificial neural networks: fundamentals, computing, design, and application. , 2000, Journal of microbiological methods.

[32]  Paolo Maggiore,et al.  A multifidelity approach to aerodynamic analysis in an integrated design environment , 2012 .

[33]  T. Simpson,et al.  Analysis of support vector regression for approximation of complex engineering analyses , 2005, DAC 2003.

[34]  Baiyu Wang A local meshless method based on moving least squares and local radial basis functions , 2015 .

[35]  Jingjing He,et al.  Lifetime distribution selection for complete and censored multi-level testing data and its influence on probability of failure estimates , 2020 .

[36]  Jean-Antoine Désidéri,et al.  Multifidelity surrogate modeling based on radial basis functions , 2017 .

[37]  Jian Ji,et al.  Moving least squares method for reliability assessment of rock tunnel excavation considering ground-support interaction , 2017 .

[38]  Hui Zhou,et al.  An adaptive global variable fidelity metamodeling strategy using a support vector regression based scaling function , 2015, Simul. Model. Pract. Theory.

[39]  Huy T. Tran,et al.  An Artificial Neural Network Approach for Generating High-Resolution Designs From Low-Resolution Input in Topology Optimization , 2019, Journal of Mechanical Design.

[40]  R. Haftka,et al.  Multifidelity Surrogate Based on Single Linear Regression , 2017, AIAA Journal.

[41]  Douglas C. Montgomery,et al.  Response Surface Methodology: Process and Product Optimization Using Designed Experiments , 1995 .

[42]  Ghina N. Absi,et al.  Simulation and Sensor Optimization for Multifidelity Dynamics Model Calibration , 2020 .

[43]  Ayse Gul Kaplan,et al.  Developing of the new models in solar radiation estimation with curve fitting based on moving least-squares approximation , 2020 .

[44]  Gang Sun,et al.  Application of deep learning based multi-fidelity surrogate model to robust aerodynamic design optimization , 2019, Aerospace Science and Technology.

[45]  Raphael T. Haftka,et al.  Variable-complexity aerodynamic optimization of a high-speed civil transport wing , 1994 .

[46]  Thomas J. Santner,et al.  Design and analysis of computer experiments , 1998 .

[47]  Thiagarajan Krishnamurthy,et al.  Comparison of Response Surface Construction Methods for Derivative Estimation Using Moving Least Squares, Kriging and Radial Basis Functions , 2013 .

[48]  T. Simpson,et al.  Comparative studies of metamodelling techniques under multiple modelling criteria , 2001 .

[49]  R. Haftka,et al.  Review of multi-fidelity models , 2016, Advances in Computational Science and Engineering.

[50]  Wenjie Zuo,et al.  Rollover crashworthiness analysis and optimization of bus frame for conceptual design , 2019 .

[51]  Robert Hewson,et al.  Multidisciplinary multifidelity optimisation of a flexible wing aerofoil with reference to a small UAV , 2014 .

[52]  Liang Gao,et al.  Metamodeling for high dimensional design problems by multi-fidelity simulations , 2017 .

[53]  Václav Skala,et al.  Radial Basis Function Approximations: Comparison and Applications , 2017, ArXiv.